Modeling the Growth of Sugar Kelp (Saccharina latissima) in Aquaculture Systems using Dynamic Energy Budget Theory
Introduction
Aquaculture is currently the fastest growing food production sector in the world and is likely to become the main seafood supply in the future (FAO, 2018). In open systems of fed species, aquaculture activities can cause concentrated fluxes of feces and feed wastage leading to eutrophication (Wu, 1995) and alteration of food webs (Herbeck et al., 2013). Open aquaculture systems composed of species that do not require supplemental feed or nutrients (i.e., primary producers and filter feeders) avoid these harms and instead can provide important ecosystem services such as removing dissolved organic and inorganic nutrients (Alleway et al., 2019). Seaweeds are of particular interest as they mitigate hypoxia from terrestrial food production systems and even protect shorelines through dampening of wave energy (Duarte et al., 2017). Outside of these ecosystem services, growing seaweed has been proposed as a way to engage a wider public audience with climate change via offsetting carbon emissions (Froehlich et al., 2019). Seaweed aquaculture has the potential to generate net positive environmental and social impacts, but this industry has been traditionally concentrated in Asian countries (FAO, 2018).
The U.S. does not produce enough aquatic plants to even register in the global production statistics (< 0.1%; FAO, 2018). In the Northeast U.S., sugar kelp (Saccharina latissima) is a local species of recent interest for food, biofuel, bioremediation, and pharmaceutical products (Forbord et al., 2012). In a single season of aquaculture growth, S. latissima blades can grow up to 60-140 cm depending on the water depth, planting time, and nutrient availability (Handå et al., 2013). Oysters, however, are the most widely aquacultured species in coastal areas of the U.S (NMFS, 2018). The Eastern oyster (Crassostrea virginica) mostly grows during the summer months when water temperatures are above 15 °C and is in a state of relative dormancy in the winter (Dame, 1972). It has been suggested that cultivation of S. latissima could complement oyster farming because of the differences in growing season with kelp growing mainly when water temperatures are below 15 °C. Therefore, kelp could provide an additional source of income without interfering with oyster production. This new industry, however, would benefit from production estimates in order to assess the biological and economic sustainability of S. latissima farming.
Bioenergetic models can provide such production estimates by studying energy fluxes and usage between the environment and the organism and within the organism. They constitute useful tools in the early development of an aquaculture activity to: assess the carrying capacity of a system before installing new farms (Grant et al., 2007; Filgueira et al., 2014), estimate production and feeding ration (Cho and Bureau, 1998), or to optimize integrated multi-trophic aquaculture (IMTA) systems (Ren et al., 2012). Forcing variables in bioenergetic modelling frameworks are of prime importance as they define the system response. In the case of S. latissima, blade growth is mainly influenced by irradiance, temperature, and nutrient concentration (Boden, 1979). Other factors such as wave action (Buck and Buchholz, 2005) and ambient light regime (Gerard, 1988) may also determine growth dynamics. In a simple predictive model, Petrell et al. (1993) estimated growth of S. latissima using a linear relationship with dissolved inorganic nitrogen concentration and a temperature correction. This model required an assumption that nitrogen dynamics are always limiting growth, thus ignoring the potential influence of irradiance. While integrating photosynthetic processes into a model can be challenging, mechanistic approaches may be more suited to capture the physiological response to environmental variability, especially in a changing environment (Denny and Helmuth, 2009).
Dynamic energy budget (DEB) theory provides a sound mechanistic basis for understanding an organism's energetics, which is used to model the flow of mass and energy through an organism from uptake to usage for maintenance, growth, reproduction, or excretion (Kooijman, 2010). This theory of metabolic organization provides a framework to examine the interactive effects of environmental nutrient concentrations and irradiance on an organism through parallel systems of nitrogen (N) and carbon (C) dynamics. Modeling autotrophs is a less common direction for the application of DEB theory. Thus, multiple reserves are necessary to accurately model matter and energy dynamics because of the different nutrient uptake pathways (Kooijman, 2010). Autotroph DEB models have been constructed for microalgae (Lorena et al., 2010, Livanou et al., 2019), phytoplankton-zooplankton interactions (Poggiale et al., 2010), calcification of a coccolithophore (Muller and Nisbet, 2014), and recently the macroalga Ulva lactuca (Lavaud et al., 2020). Broch and Slagstad (2012) were the first to develop a dynamic bioenergetic model for S. latissima, borrowing concepts from DEB theory with the aim of creating a tool for optimizing aquaculture production of Norwegian S. latissima. These authors based their model structure on a Droop's cell quota model completed by numerous empirical and allometric relationships to simulate growth of S. latissima, but this simplification did not increase parsimony (i.e., reduce the number of model parameters). Using a DEB framework, however, ensures theoretical coherence (i.e., mechanistic description of metabolic processes) and ease of model transference to other regions through less re-calibration.
Our objective with this study is to develop a bioenergetic model for S. latissima growth using the mechanistic properties of DEB theory. Specifically, we aim to calibrate the macroalga DEB model presented by Lavaud et al. (2020) to field data on kelp from Rhode Island (U.S.A.). The application of this model to another species from a different environment constitutes an important step in the validation of this model structure. The resulting model allows for growth predictions based on environmental inputs and has the potential to support the sustainable aquaculture industry, particularly with regard to site selection.
Section snippets
Dynamic energy budget model assumptions
The S. latissima model is based on a DEB model developed for sea lettuce by Lavaud et al. (2020). The core structure of the S. latissima model tracks the uptake of carbon (C) and nitrogen (N), their assimilation into specific reserves and allocation to growth or maintenance or their excretion (Fig. 1). The variables that depict the state of the model are the mass of structure MV (in mol V, moles of structure), Nitrogen reserve density (in mol N per mol V), and Carbon reserve density (in
Model calibration: literature data
The Arrhenius relationship fit to the compiled literature data (Table 3) reflected maximum physiological rates at temperatures around 13°C (Fig. 4). The lower boundary of the temperature tolerance range in the Arrhenius relationship was 0°C, and the upper boundary was 13.39°C. The rather low value for the upper boundary indicates that the optimum temperature is close to the upper limit of the tolerance range for this species. However, the shape of the curve past this point implies that the
Discussion
Aquaculture development represents a key role in expanding U.S. sustainable food production and macroalgae can provide high returns when the proper growth conditions exist. Understanding and predicting the growth dynamics of S. latissima can provide the aquaculture industry with a powerful predictive tool for estimating production potential. This model is the first attempt to apply Dynamic Energy Budget (DEB) theory to a macroalga of the order Laminariales. The process-based model presented in
Author statement
Celeste T. Venolia: Conceptualization, Methodology, Formal analysis, Validation, Visualization, Data curation, Writing – Original Draft, Writing – Review & Editing
Romain Lavaud: Conceptualization, Software, Methodology, Visualization, Writing – Original Draft, Writing – Review & Editing
Lindsay A. Green-Gavrielidis: Funding acquisition, Supervision, Investigation, Conceptualization, Data curation, Writing – Original Draft, Writing – Review & Editing
Carol Thornber: Funding acquisition,
Funding
This work is supported by a National Oceanic and Atmospheric Administration Saltonstall-Kennedygrant [17GAR008], NSERC grant [497065-2016], the National Science Foundation under EPSCoR Cooperative Agreement #OIA-165522 and the U.S. Department of Agriculture's National Institute of Food and Agriculture, Hatch Formula project 1011478.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This study would not have been possible without the valuable contributions of the aquaculture farmers working with us to grow kelp for two years: Cindy and John West of Moonstone Oysters, Russ and Thomas Blank of Rome Point Oyster Farm, Trip Whilden of Wickford Oyster Co, and Perry Russo of Matunuck Oyster Farm. Dave Ullman and Chris Kincaid provided assistance in the initial project development. Thomas Guyondet facilitated collaborations around this project. Dawn Outram at the Marine Science
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Present address: Department of Biology and Biomedical Sciences, Salve Regina University, Newport, RI, USA